An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation
In this paper, we are concerned with microwave subsurface imaging achieved by inverting the linearized scattering operator arising from the Born approximation. In particular, we consider the important question of reducing the required data to achieve imaging. This can help to reduce the radar system...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/12/3012 |
| _version_ | 1827735809488322560 |
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| author | Maria Antonia Maisto Chandan Bhat Raffaele Solimene |
| author_facet | Maria Antonia Maisto Chandan Bhat Raffaele Solimene |
| author_sort | Maria Antonia Maisto |
| collection | DOAJ |
| description | In this paper, we are concerned with microwave subsurface imaging achieved by inverting the linearized scattering operator arising from the Born approximation. In particular, we consider the important question of reducing the required data to achieve imaging. This can help to reduce the radar system’s cost and complexity and mitigate the imaging algorithm’s computational burden and the needed storage resources. To cope with these issues, in the framework of a multi-monostatic/multi-frequency configuration, we introduce a new spatial sampling scheme, named the warping method, that allows for a significant reduction in spatial measurements compared to other literature approaches. The basic idea is to introduce some variable transformations that “warp” the measurement space so that the reconstruction point-spread function obtained by adjoint inversion is recast as a Fourier-like transformation, which provides insights into how to achieve the sampling. In our previous contributions, we focused on presenting and checking the theoretical background with simple numerical examples. In this contribution, we briefly review the key components of the warping method and present its experimental validation by considering a realistic subsurface scattering scenario for the case of a buried water pipe. Essentially, we show that the latter succeeds in reducing the number of data compared to other approaches in the literature, without significantly affecting the reconstruction results. |
| first_indexed | 2024-03-11T01:58:51Z |
| format | Article |
| id | doaj.art-5a8f308bcbb6450fa81bc24b48ea52ad |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T01:58:51Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5a8f308bcbb6450fa81bc24b48ea52ad2023-11-18T12:25:06ZengMDPI AGRemote Sensing2072-42922023-06-011512301210.3390/rs15123012An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental ValidationMaria Antonia Maisto0Chandan Bhat1Raffaele Solimene2Dipartimento di Ingegneria, Università della Campania, 81031 Aversa, ItalyDepartment of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036, IndiaDipartimento di Ingegneria, Università della Campania, 81031 Aversa, ItalyIn this paper, we are concerned with microwave subsurface imaging achieved by inverting the linearized scattering operator arising from the Born approximation. In particular, we consider the important question of reducing the required data to achieve imaging. This can help to reduce the radar system’s cost and complexity and mitigate the imaging algorithm’s computational burden and the needed storage resources. To cope with these issues, in the framework of a multi-monostatic/multi-frequency configuration, we introduce a new spatial sampling scheme, named the warping method, that allows for a significant reduction in spatial measurements compared to other literature approaches. The basic idea is to introduce some variable transformations that “warp” the measurement space so that the reconstruction point-spread function obtained by adjoint inversion is recast as a Fourier-like transformation, which provides insights into how to achieve the sampling. In our previous contributions, we focused on presenting and checking the theoretical background with simple numerical examples. In this contribution, we briefly review the key components of the warping method and present its experimental validation by considering a realistic subsurface scattering scenario for the case of a buried water pipe. Essentially, we show that the latter succeeds in reducing the number of data compared to other approaches in the literature, without significantly affecting the reconstruction results.https://www.mdpi.com/2072-4292/15/12/3012microwave imagingradar systemsinverse scatteringsampling strategy |
| spellingShingle | Maria Antonia Maisto Chandan Bhat Raffaele Solimene An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation Remote Sensing microwave imaging radar systems inverse scattering sampling strategy |
| title | An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation |
| title_full | An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation |
| title_fullStr | An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation |
| title_full_unstemmed | An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation |
| title_short | An Insight into the Warping Spatial Sampling Method in Subsurface Radar Imaging and Its Experimental Validation |
| title_sort | insight into the warping spatial sampling method in subsurface radar imaging and its experimental validation |
| topic | microwave imaging radar systems inverse scattering sampling strategy |
| url | https://www.mdpi.com/2072-4292/15/12/3012 |
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